SUMMARY Multiple sclerosis (MS) is a multifactorial inflammatory disease of the central nervous system characterized by demyelination, gliosis, axonal loss, and progressive neurological dysfunction. The etiology of MS involves both genetic and environmental risk factors (ERFs). The fact that monozygotic twin studies indicate that ~30% of MS risk can be accounted for by genetics suggests that ~70% of MS risk is due to environment or gene-by- environment (GE) interactions. Epidemiological data strongly implicate MS-ERFs acting upon the genetically susceptible background at the population level, and have documented a 3-6 fold increase in MS incidence and prevalence in females over the last 50-70 years, while remaining relatively stable in males. This rate of change clearly implicates MS-ERFs that are preferentially affecting MS susceptibility through GESex (GES) interactions. The ideal study design to assess the role of GES interactions in the etiopathogenesis of MS requires a long-term cohort study where data is available on exposure to MS-ERFs across multiple developmental periods. Given that the prevalence of MS is ~90/100,000 makes such studies unwieldy if not impossible to perform due to lack of statistical power. Consequently, an appropriate animal model is required to validate and incisively analyze the sex- and cell-type-specific mechanisms impacted by GES interactions in the pathogenesis of MS. In this regard, we have shown in mice that experimental allergic encephalomyelitis (EAE), the principal autoimmune model of MS, recapitulates with high fidelity many of the elements underlying MS pathogenesis, including the genetic architecture and role for cell-mediated immune mechanisms. Additionally, our pioneering studies showing that EAE can be used to model the effects of both GMS-intrinsic (sex and age at immunization) and GMS-extrinsic (season of immunization. post-natal maternal environment, and dietary Na+) interactions clearly establishes EAE as a physiologically relevant model that can be used to bridge the intractable gap in our understanding of GES interactions in MS etiopathogenesis. In this application, we will test the hypothesis that exposure to Epstein-Barr virus (EBV) infection and cigarette smoking (CS), the two MS-ERFs identified as having the strongest consistent evidence of an association in a recent large meta- analysis involving 44 unique meta-analyses including 416 primary studies of different MS-ERFs, influence MS susceptibility through GES interactions. Male and female chromosome (Chr) substitution strains covering the highly polymorphic wild-derived inbred PWD/PhJ nuclear and mitochondrial genomes on the C57BL/6J background (B6-ChrPWD) will be used to carry out genome-wide physical mapping to identify Chrs harboring genes that interact with gamma herpes virus 68 (?HV-68), the murine homologue of EBV, (Specific Aim 1) and CS (Specific Aim 2) to influence EAE outcomes and associated immunopathologic phenotypes. Additionally, using male and female mice we will identify sex- and cell-type-specific (APCs, B cells, CD4+ and CD8+ T cells, Tregs, and microglial) genes whose expression (eQTL) is influenced by adult exposure to ?HV-68 and CS. These data will provide a comprehensive view of the genetic factors and interactions that are unique to different MS- ERFs, as well as those that are shared among risk factors. By integrating the differential sex- and cell-type- specific gene networks impacted by MS-ERF exposure with the identification of candidate genes associated with EAE susceptibility, we will be able to identify and model the relevant evolutionarily conserved sex- and cell-type- specific gene networks operating in the etiopathogenesis of MS. Moreover, the overall outcome of the studies proposed in this application will provide a basic format for modeling GES interactions in the pathogenesis of MS as additional MS-ERFs are identified, and as a function of simultaneous exposure to multiple MS-ERFs. Such outcomes will undoubtedly enhance not only our understanding of the etiopathogenesis of MS, but will provide a wealth of data relevant to treating MS and possibly preventing it based on exposure to ERFs.